Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification

Authors
Lee, Byeong-HooJeong, Ji-HoonLee, Seong-Whan
Issue Date
2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Electroencephalography; Feature extraction; Machine learning; Indexes; Visualization; Training; Protocols; Brain-computer interface (BCI); electroencephalogram (EEG); motor imagery (MI); convolutional neural network (CNN); weighted ensemble learning
Citation
IEEE ACCESS, v.8, pp.134524 - 134535
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
8
Start Page
134524
End Page
134535
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/58933
DOI
10.1109/ACCESS.2020.3011140
ISSN
2169-3536
Abstract
A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Motor imagery (MI) paradigm is widely used in non-invasive BCI to control external devices by decoding user intentions. The traditional MI-BCI problem is to obtain enough EEG data samples for adopting deep learning techniques, as electroencephalography (EEG) data have intricate and non-stationary properties that can cause a discrepancy between different sessions of data. Because of the discrepancy, the recorded EEG data with different sessions cannot be treated as the same. In this study, we recorded a large intuitive EEG dataset that contained nine types of movements of a single-arm across 12 subjects. We proposed a SessionNet that learns generality with EEG data recorded over multiple sessions using feature similarity to improve classification performance. Additionally, the SessionNet adopts the principle of a hierarchical convolutional neural network that shows robust classification performance regardless of the number of classes. The SessionNet outperforms conventional methods on 3-class, 5-class, and two types of 7-class and 9-class of a single-arm task. Hence, our approach could demonstrate the possibility of using feature similarity based on a novel ensemble learning method to train generality from multiple session data for better MI classification performance.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Lee, Seong Whan photo

Lee, Seong Whan
인공지능학과
Read more

Altmetrics

Total Views & Downloads

BROWSE